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Quantum Variational Circuits (QVCs) are often claimed as one of the most potent uses of both near term and long term quantum hardware.
An efficient method for finding the minimum of a function of several variables without calculating derivatives
Michael JD Powell · 1964
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Function minimization by conjugate gradients
Reeves Fletcher and Colin M Reeves · 1964
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A simplex method for function minimization
John A Nelder and Roger Mead · 1965
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Newton-type minimization via the lanczos method
Stephen G Nash · 1984
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A software package for sequential quadratic programming
Dieter Kraft et al · 1988
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Algorithms for quantum computation: discrete logarithms and factoring
Peter W Shor · 1994
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A direct search optimization method that models the objective and constraint functions by linear interpolation
Michael JD Powell · 1994
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A limited memory algorithm for bound constrained optimization
Richard H Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu · 1995
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An overview of the simultaneous perturbation method for efficient optimization
James C Spall · 1998
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On the implementation of an algorithm for large-scale equality constrained optimization
Marucha Lalee, Jorge Nocedal, and Todd Plantenga · 1998
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Numerical optimization
Jorge Nocedal and Stephen Wright · 2006
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Quantum clustering algorithms
Esma Aïmeur, Gilles Brassard, and Sébastien Gambs · 2007
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman, Geoffrey Hinton, et al · 2012
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Ad click prediction: a view from the trenches
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
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Quantum support vector machine for big data classification
Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd · 2014
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Quantum principal component analysis
Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost · 2014
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A variational eigenvalue solver on a photonic quantum processor
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien · 2014
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A quantum approximate optimization algorithm
Edward Farhi, Jeffrey Goldstone, and Sam Gutmann · 2014
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Demonstration of quantum advantage in machine learning
Diego Ristè, Marcus P Da Silva, Colm A Ryan, Andrew W Cross, Antonio D Córcoles, John A Smolin, Jay M Gambetta, Jerry M Chow, and Blake R Johnson · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
Cited alongside, same era.
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Abhinav Kandala, Antonio Mezzacapo, Kristan Temme, Maika Takita, Markus Brink, Jerry M Chow, and Jay M Gambetta · 2017
Cited alongside, same era.
Simulating physics with computers
Richard P Feynman · 2018
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Quantum computing in the nisq era and beyond
John Preskill · 2018
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Quantum generative adversarial networks
Pierre-Luc Dallaire-Demers and Nathan Killoran · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R McClean, Sergio Boixo, Vadim N Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
Cited alongside, same era.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Strong quantum computational advantage using a superconducting quantum processor
Yulin Wu, Wan-Su Bao, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, et al · 2021
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Quantum circuits with many photons on a programmable nanophotonic chip
JM Arrazola, V Bergholm, K Brádler, TR Bromley, MJ Collins, I Dhand, A Fumagalli, T Gerrits, A Goussev, LG Helt, et al · 2021
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How to factor 2048 bit rsa integers in 8 hours using 20 million noisy qubits
Craig Gidney and Martin Ekerå · 2021
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Variational quantum algorithms
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Quantum supremacy using a programmable superconducting processor
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al · 2019
Cited alongside, same era.
Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
Cited alongside, same era.
Deep reinforcement learning for robust quantum optimization
Vegard B Sørdal and Joakim Bergli · 2019
Cited alongside, same era.
On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar · 2019
Cited alongside, same era.
Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Cited alongside, same era.
On the convergence proof of amsgrad and a new version
Phuong Thi Tran et al · 2019
Cited alongside, same era.
Marco Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, et al · 2021
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Noisy intermediate-scale quantum (nisq) algorithms
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S Kottmann, Tim Menke, et al · 2021
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Playing atari with hybrid quantum-classical reinforcement learning
Owen Lockwood and Mei Si · 2021
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Quantum agents in the gym: a variational quantum algorithm for deep q-learning
Andrea Skolik, Sofiene Jerbi, and Vedran Dunjko · 2021
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Parametrized quantum policies for reinforcement learning
Sofiene Jerbi, Casper Gyurik, Simon Callum Marshall, Hans J Briegel, and Vedran Dunjko · 2021
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Effect of barren plateaus on gradient-free optimization
Andrew Arrasmith, M Cerezo, Piotr Czarnik, Lukasz Cincio, and Patrick J Coles · 2021
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Training variational quantum algorithms is np-hard
Lennart Bittel and Martin Kliesch · 2021
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Quantumcircuitopt: An open-source framework for provably optimal quantum circuit design
Harsha Nagarajan, Owen Lockwood, and Carleton Coffrin · 2021
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Quantum advantage in learning from experiments, 2021
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, and Jarrod R. McClean · 2021
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General parameter-shift rules for quantum gradients
David Wierichs, Josh Izaac, Cody Wang, and Cedric Yen-Yu Lin · 2021
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Optimizing quantum variational circuits with deep reinforcement learning
Owen Lockwood · 2021
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Tensorflow quantum: A software framework for quantum machine learning, 2021
Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao, Roeland Wiersema, Hsin-Yuan Huang, Jarrod R. McClean, Ryan Babbush, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, and Masoud Mohseni · 2021
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Performance comparison of optimization methods on variational quantum algorithms
Xavier Bonet-Monroig, Hao Wang, Diederick Vermetten, Bruno Senjean, Charles Moussa, Thomas Bäck, Vedran Dunjko, and Thomas E O’Brien · 2021
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Estimating the gradient and higher-order derivatives on quantum hardware
Andrea Mari, Thomas R Bromley, and Nathan Killoran · 2021
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Speeding up learning quantum states through group equivariant convolutional quantum ansatze
Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, and Risi Kondor · 2021
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Simulations of quantum circuits with approximate noise using qsim and cirq
Sergei V Isakov, Dvir Kafri, Orion Martin, Catherine Vollgraff Heidweiller, Wojciech Mruczkiewicz, Matthew P Harrigan, Nicholas C Rubin, Ross Thomson, Michael Broughton, Kevin Kissell, et al · 2021
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